An Accurate Iris Segmentation Framework under
Relaxed Imaging Constraints using Total Variation Model

Iris recognition is one of the most accurate and widely employed approaches for the automated personal identification.
In this work we develop a new and more accurate iris segmentation framework to automatically segment iris region from the face images acquired with relaxed imaging under visible or near-infrared illumination, which provides strong feasibility for applications in surveillance, forensics and the search for missing children, etc. The proposed framework is built on a novel total-variation based formulation which uses l1 norm regularization to robustly suppress noisy texture pixels for the accurate iris localization.
The intermediate results from two automatically detected eye samples are
reproduced in the following figure.

A series of novel and robust post processing operations are introduced to more accurately localize the limbic boundaries. Our experimental results on three publicly available databases, i.e., FRGC, UBIRIS.v2 and CASIA.v4-distance, achieve significant performance improvement in terms of iris segmentation accuracy over the state-of-the-art approaches in the literature. Besides, we have shown that using iris masks generated from the proposed approach helps to improve iris recognition performance as well. Unlike prior work, all the implementations in this paper are made publicly available to further advance research and applications in biometrics at-d-distance..

Implementation Codes for At-A-Distance Iris Segmentation Algorithm

You can download the
implementation codes used to develop and evaluate the algorithm described in
this paper. The details and the link for the files appear
in the following. The codes are in Matlab and provides all the parameters
required to reproduce the results.

ReadMe File

A readme.txt file provides
details on running the programs on each of the three distantly acquired iris/eye
images
databases employed in this work.

Download and Copyright

The implementation codes are made available for the researchers from December 2015
onwards. Interested researchers should downlload the
tvmiris.zip file and follow
the steps detailed in the readme file. This implementation or software is
provided on 'as it is' basis and does not include any warranty of any kind.